Social life networks presentation at fb 110713

4,061 views

Published on

Presentation given at Facebook on Social Life Networks

Published in: Technology
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
4,061
On SlideShare
0
From Embeds
0
Number of Embeds
373
Actions
Shares
0
Downloads
63
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

Social life networks presentation at fb 110713

  1. 1. andMiddle of the Pyramid Ramesh Jain WithSeveral Collaborators
  2. 2. 1.  Networks2.  Computing Networks3.  Social Networks4.  Social Life Networks5.  Major Challenge: Micro-events to Situations6.  Our approach7.  Going Forward
  3. 3.   Different media and information sources  Strongly emerging participatory culture  Collective knowledge and intelligence of society
  4. 4.   People  Things  Events
  5. 5. Documents, Data, Events   Document created by Humans.   Text, Music, Movies   Data collected by Humans   Photos, Audio, …  Events happen.   Most documents describe events and objects in those.   Most data is collected for events.
  6. 6.   Facebook, Twitter, Google +, …  Sensor networks   Billions of sensor getting connected  Ambitious projects   Planetary Skin by Cisco and NASA   Smart Planet by IBM
  7. 7.   Can things in real world be connected to other things?  Does this even make sense?
  8. 8.   Five Senses connect us to the world.  We use our sensors (vision, audio, …) to experience the world.  Sensors could be the interface between the Cyberspace and the Real World.  Sensors are placed for ‘detecting events’.   How do you decide what sensors to put at any place?   Would you put a sensor if nothing interesting ever happens at a place?
  9. 9. People Structural   Things PlacesCausal     Time Experiences Experiential   Events
  10. 10.   Data  Objects  Relationships and Events
  11. 11.   Objects -- popular in the West.  Relationships and Events – popular in the East.  Objects and Events – seems to be the new trend.  The Web has re-emphasized the importance of every object and event being connected to others -- East Meets West.
  12. 12.   Consider a Web in which each node   Is an event   Has informational as well as experiential data   Is connected to other nodes using   Referential links   Structural links   Relational links   Causal links   Explicit links can be created by anybody  This EventWeb is connected to other Webs.
  13. 13.   SN are web-based services that allow individuals to:   construct a public or semi-public profile within a bounded system,   articulate a list of other users with whom they share a connection, and   view and traverse their list of connections and those made by others within the system.  The nature and nomenclature of these connections may vary from site to site. Node in a SN Professor at University of California, IrvineStudied Electronics and Communications at Indian Institute of Technology, KharagpurLives in Professor at University of California, IrvineStudied Electronics and Communications at Indian Institute of Technology, Irvine, CaliforniaMarried to Sudha JainKnows English, HindiFrom NagpurBorn on June 8 KharagpurLives in Irvine, CaliforniaMarried to Sudha JainKnows English, HindiFrom NagpurBorn on June 8
  14. 14. Professor atUniversity of California,IrvineStudied Electronics andCommunications at IndianInstitute of Technology,KharagpurLives in Irvine,CaliforniaMarried to SudhaJainKnows English,HindiFrom NagpurBorn onJune 8
  15. 15. Connecting People
  16. 16. My GrandparentsR My BFF! OMG! WSJ May 9, 2011
  17. 17. Have been reporting events as micro-blogs Massive collection of events.
  18. 18. Time
  19. 19. Does the flap of a butterfly’s wings in Brazil set off a tornado in Texas?
  20. 20. FROM TWEETS TO REVOLUTIONS
  21. 21. Atomic and Composite Events Time
  22. 22. Most attention by Top 1.5 Technologists – so Billion far. Middle of the PyramidMiddle 4 Billion (MOP): Ready, BUT …Bottom 2 Billion Not Ready
  23. 23. Highest BasicEvery human society should be provided with the first two. Other stages follow only after that.
  24. 24.   Resources   Physical: food, water, goods, …   Informational: Wikipedia, Doctors, …   Transportation   Employment   Spiritual  Timeliness  Efficiency
  25. 25. Connecting Information PeopleAggregation Situation Alerts and DetectionCompositionAnd QueriesResources
  26. 26.   All traditional Persistent Web sources  Micro blogs   Status updates   Tweets   Streams  Micro Events   All sensors ‘Chirping’   Internet of Things  People input in any form
  27. 27.   Result of   Exponential growth in connectivity   Sensor Networks  Evolution of Sharing Culture  Technology for Collective Knowledge
  28. 28.   Each Micro-blog:   What’s on your mind?   What’s happening?   Share What’s New …  Really an event reported by Humans.  Can associate experiential data along with information.  Time and location can be associated.
  29. 29.   Billions of disparate kinds of sensors being placed everywhere.  Each sensor detects ‘basic events’ and broadcasts it in a simple form.  Develop a system to process these micro-events and make them useful.
  30. 30.   ‘Chirps’ could be of different types  Define behaviors like:   Heavy traffic   Popular event going on   People leaving X area   Violence starting   ...  Use for Macro-behvior analysis
  31. 31. Representa*ons Examples More   abstrac*on,   Level  3:   Number of accidents Proper*es Symbolic  Rep.   Less  detail (Events) Characteriza*ons Level  2:   Average speed, Transforma*ons Aggrega*on   Proper*es Occupancy rate (Emage)   Level  1:  Unified   representa*on   Proper*es Speed at Exit 7 (STT  Data)Less  abstrac*on,  More  detail Level 0: Raw data Loop   …   sensors e.g.  Waze,  511 33
  32. 32. Representa*ons Examples More   abstrac*on,   Level  3:   Proper*es Badly affected areas Symbolic  Rep.   Less  detail (Events) Characteriza*ons Level  2:   Mean traffic smoke Transforma*ons Aggrega*on   Proper*es exposure time (Emage)   Per capita Asthma tweets Level  1:  Unified   representa*on   Proper*es Pollen count in NYC (STT  Data)Less  abstrac*on,  More  detail Level 0: Raw data Tweets Pollen   Fire   Traffic congestion counts reports 34
  33. 33.   From Micro-behavior to Macro-behavior  Studied in many fields:   Economics   Thermodynamics   Systems Biology  Web facilitates this for many novel applications
  34. 34.   Divide space (world) into small Pixels of appropriate size.  Assume that each event is a particle of a specific type. Create a Social Image for specific type of events.  A time-ordered sequence of these emages will be similar to a video representing spatio- temporal changes in events of that type.
  35. 35. S.  No   Operator   Input   Output  1   Selection  σ   Temporal     Temporal     E-­‐mage  Set   E-­‐mage  Set  2   Arithmetic    &   K*Temporal  E-­‐mage   Temporal  E-­‐mage  Set   Logical⊕   Set  3   Aggregation  α   Temporal  E-­‐mage  set   Temporal  E-­‐mage  Set  4   Grouping  γ   Temporal  E-­‐mage  Set   Temporal  E-­‐mage  Set  5   Characterization  :   • Spatial  φ   • Temporal  E-­‐mage  Set   • Temporal  Pixel  Set   • Temporal  τ   • Temporal  Pixel  Set   • Temporal  Pixel  Set  6   Pattern  Matching  ψ   • Spatial  φ   • Temporal  E-­‐mage  Set   • Temporal  Pixel  Set   • Temporal  τ   • Temporal  Pixel  Set   • Temporal  Pixel  Set   38
  36. 36.   Spatio temporal variation: Event detection
  37. 37. into ‘high’ and ‘low ’activity zones.
  38. 38. Macro situation Alert Level=High Date=12/09/10 Micro event Situational Control Action e.g. “Arrgggh, I controller “Please visit have a sore nearest CDC throat” • Goal center at 4th St (Loc=New York, • Macro Situation immediately” Date=12/09/10) • RulesLevel 1 personal threat + Level 3 Macro threat -> Immediateaction
  39. 39. 1.  For centralized agencies   Most of what we have done so far2.  For individuals who subscribe   Asthma3.  Alerts based on (implicit subscription): user’s (FB) interests, events attending, trips, sports, music, fan pages…   Maybe we can derive asthma, from FB details?4.  I’m bored! What’s around me? (based on a generic interest set)   NowLedger
  40. 40.   Brand monitoring  Epidemic monitoring  Political campaigns  Decision making: e.g. iphone new store
  41. 41.   Asthma  Wildfires  Traffic  Dating  Coupons  …
  42. 42.   Concerts, Campaigns, Memorabilia, Book stores, (anything you are a fan of)  Your friends  Only show content whose ‘information’ is high. If your friend normally lives 500 miles away and is NOW within 5 miles then alert. If he is always within 2 miles, don’t alert.
  43. 43.   Food  Drinks  Movies  Concerts  Academic  Professional
  44. 44. Direct the innovation and R&D towards the needs of the World’s middle class – the Middle of the Pyramid (MOP). Expand the Middle to cover the Bottom.
  45. 45. Health Education Agriculture Social For addressing all life elements.
  46. 46.   Resource ingestion  Situation analysis  ‘Real Time’ matching of needs and availability of resources  Interaction environments  User engagement, … and many others
  47. 47.   Event Based  Experience Centric  Centered around YOU  No Country Left Behind
  48. 48. Contact: jain@ics.uci.edu

×